The kappa statistic: a second look
Computational Linguistics
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Using appraisal groups for sentiment analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Creating subjective and objective sentence classifiers from unannotated texts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Automatically generating annotator rationales to improve sentiment classification
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Sentiment strength detection for the social web
Journal of the American Society for Information Science and Technology
Discourse structure and language technology
Natural Language Engineering
A Framework to Extract Arguments in Opinion Texts
International Journal of Cognitive Informatics and Natural Intelligence
A Framework to Extract Arguments in Opinion Texts
International Journal of Cognitive Informatics and Natural Intelligence
Hi-index | 0.00 |
We present a taxonomy and classification system for distinguishing between different types of paragraphs in movie reviews: formal vs. functional paragraphs and, within the latter, between description and comment. The classification is used for sentiment extraction, achieving improvement over a baseline without paragraph classification.